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Hintergrund: Das Auftreten einer kardialen autonomen Dysfunktion nach einem akutem ischämischen Schlaganfall (AIS) geht mit einer ungünstigen Prognose und einer erhöhten Mortalität einher. In der vorliegenden Arbeit wurde die Hypothese untersucht, dass Herzfrequenzvariabilitäts (HRV)-Biofeedback die autonome Herzfunktion nach Schlaganfall verbessern kann. Methodik/Design: 48 AIS-Patienten erhielten unter randomisierten Bedingungen entweder HRV- oder Sham-Biofeedback (1:1) zusätzlich zur standardisierten Stroke Unit Versorgung. Bei sämtlichen Studienteilnehmern wurde vor Beginn der ersten und nach Abschluss der letzten Biofeedbacksitzung eine autonome Funktionsmessung durchgeführt, die neben der Messung der HRV auch eine Erfassung der autonomen vasomotorischen (die neurovaskuläre Regulation der arteriellen Blutgefäßweite erfassenden) und sudomotorischen (die neuronale Regulation der Schweißdrüsenfunktion quantifizierenden) Funktion beinhaltete. Die HRV wurde mittels Standardabweichung der NN-Intervalle (SDNN), der Standardabweichung der Differenzen benachbarter NN-Intervalle (SD of ΔNN), der Quadratwurzel des Mittelwerts aus der Summe der Quadrate der Differenzen zwischen benachbarten NN-Intervallen (RMSSD), sowie mittels des Variationskoeffizienten der R-R-Intervalle (CVNN) untersucht. Während die Parameter SDNN und RMSSD vorwiegend parasympathisch determinierten Indikatoren der HRV entsprechen, stellt der CVNN einen kompositen Parameter der sympathischen und der parasympathischen Aktivität dar. Darüber hinaus wurde eine Frequenzanalyse der HRV durchgeführt, um die Frequenzbänder der HRV differenziert zu erfassen und den Wirkmechanismus des HRV-Biofeedbacks auf die kardiale autonome Funktion zu charakterisieren. Die beiden sympathisch regulierten Funktionen der Vaso- und Sudomotorik wurden nach sympathischer Aktivierung gemessen, wobei die vasomotorische Funktion mittels Photoplethysmographie (PPG) der vasokonstriktorischen Reaktion (VCR) und die sudomotorische Hautleitwertänderung (SSR) durch Ableiteelektroden erfasst wurde. Die Bewertung des Schweregrades der autonomen Symptome durch den Survey of Autonomic Symptoms (SAS; TIS: Gesamtschwere autonomer Symptome) und des funktionellen Defizites durch die modifizierte Rankin-Skala (mRS) erfolgten vor Beginn der Intervention und drei Monate nach Interventionsbeendigung. Das Studienprotokoll wurde vor Beginn der Untersuchung in der Datenbank clinicaltrials.gov hinterlegt [clinicaltrials.gov identifier: NCT03865225]. Ergebnisse: 48 AIS-Patienten (19 Frauen; Alter im Median 69 [Interquartilsbereich 18.0] Jahre) wurden in die Untersuchung eingeschlossen. Angesichts einer hohen Adhärenz und Verträglichkeit der HRV-Biofeedbackanwendung (<0.1% fehlende Daten, keine Studienabbrühe während der Hospitalisierungsphase, unerwünschte Wirkungen: leichtgradig n=1/48) ließ sich das HRV-Biofeedbackverfahren unproblematisch in das das Setting einer multidisziplinären Stroke Unit integrieren. Die Anwendung von HRV-Biofeedback führte zu einer Erhöhung der HRV unter metronomischer Atmung (SDNN: 34,1 [45.0] ms Baseline vs. 43,5 [79.0] ms post-Intervention, p=0,015; SD of ΔNN: 29.3 [52.7] ms baseline vs. 46.4 [142.1] ms post-intervention, p=0.013; RMSSD: 29,1 [52.2] ms Baseline vs. 46,0 [140.6] ms post-Intervention, p=0.015; nicht-signifikanter Trend einer Erhöhung des CVNN: 4.1 [5.1] % Baseline vs. 5.4 [7.2] % post-Intervention, p=0.052), die nach dem Sham-Biofeedback nicht zu verzeichnen war (p=nicht signifikant (ns)). Zudem ergab die Frequenzanalyse der HRV unter metronomischer Atmung nach HRV-Biofeedback einen Anstieg im Niederfrequenzband (LF) (484.8 [1941.4] ms2 Baseline vs. 1471.3 [3329.9] ms2 post-Intervention, p=0.019) und der Total Power (1273.9 [3299.2] ms2 Baseline vs. 1771.5 [13038.8] ms2 post-Intervention, p=0.022), der in der Sham-Biofeedbackgruppe nicht beobachtet wurde (p=ns). In beiden Studiengruppen zeigte sich keine Veränderung der sympathischen Funktionen der Sudo- und Vasomotorik (p=ns). HRV-Biofeedback führte zu einer Linderung des Schweregrades autonomer Symptome drei Monate nach der Intervention (TIS: 7.5 [7.0] Baseline vs. 3.5 [8.0] Follow-Up, p=0.029), welche in der Sham-Biofeedbackgruppe ausblieb (p=ns). Erwartungsgemäß zeigten beide Studiengruppen nach drei Monaten eine Besserung der funktionellen Defizite (HRV-Biofeedbackgruppe, mRS: 2.0 [1.0] Baseline vs. 0.0 [2.0] Follow-Up, p=0.023; Sham-Biofeedbackgruppe, mRS: 2.2 [2.0] Baseline vs. 1.0 [2.0] Follow-Up, p=0.0005). Schlussfolgerungen: Die Integration von HRV-Biofeedback in die multidisziplinäre Standardversorgung einer Schlaganfallstation führte bei Patienten mit AIS zu einer Verbesserung der kardialen autonomen Funktion. Diese funktionelle Verbesserung wurde wahrscheinlich durch einen vorwiegend parasympathischen Mechanismus vermittelt und ging mit einer anhaltenden Linderung autonomer Symptome einher.:1.EINLEITUNG 1 2. HINTERGRUND 4 2.1 Schlaganfall: Pathophysiologie und klinische Bedeutung 4 2.1.1 Definition und Klassifikation 4 2.1.2 Epidemiologie 7 2.1.3 Lokalisationsbezogene klinische Präsentation 9 2.1.4 Therapie 13 2.1.5 Risikofaktoren 16 2.2 Autonomes Nervensystem (ANS): Grundlagen und Beeinträchtigungen bei Schlaganfallpatienten16 2.2.1 Anatomische und physiologische Grundlagen 17 2.2.1.1 Sympathisches Nervensystem (SNS) 20 2.2.1.2 Parasympathisches Nervensystem (PaNS) 22 2.2.1.3 Enterisches Nervensystem (ENS) 23 2.2.2 Autonome Dysfunktion beim Schlaganfall 24 2.3 Herzratenvariabilität (HRV): Ein diagnostisches Target der kardialen autonomen Funktion 25 2.3.1 Definition 25 2.3.2 Relevanz 27 2.3.3 Anwendungsbereiche 28 2.4 Biofeedback: Allgemeine Therapieprinzipien und HRV-spezifische Anwendung 29 2.4.1 Definition 29 2.4.2 Anwendungsbereiche 29 2.4.3 Herzratenvariabilitäts-gestütztes Biofeedback 31 3. FORSCHUNGSLÜCKE („RESEARCH GAP“) 32 4. ZIELSETZUNG UND HYPOTHESEN 32 5. METHODIK 33 5.1 Ethik 33 5.2 Studiendesign und Messprotokoll 34 5.3 Patienten 36 5.3.1 Patientenrekrutierung 36 5.3.2 Einschlusskriterien 36 5.3.3 Ausschlusskriterien 36 5.3.4 Patienteninformation und -einverständniserklärung 37 5.3.5 Randomisierung 37 5.4 Funktionsmessungen 37 5.4.1 Funktionen des autonomen Nervensystems 37 5.4.1.1 Kardiale autonome Funktion: Herzratenvariabilität (HRV) 40 5.4.1.2 Sudomotorische autonome Funktion: Sympathetic Skin Response (SSR) 44 5.4.1.3 Vasomotorische autonome Funktion: Photoplethysmographie (PPG) 46 5.4.2 Symptomschwere und funktionelle Beeinträchtigung 48 5.4.2.1 Autonomes Outcome: Survey of Autonomic Symptoms (SAS) 48 5.4.2.2 Funktionelles Outcome: modified Rankin Scale (mRS) 49 5.4.2.3 Neurologisches Outcome: National Institutes of Health Stroke Scale (NIHSS) 49 5.5 Studienintervention: Herzratenvariabilitätsgestütztes Biofeedback 50 5.6 Statistische Analyse 51 6. ERGEBNISSE 52 6.1 Demographische Daten und Baseline-Charakteristika 52 6.2 Rekrutierung und fehlende Daten 54 6.3 Autonome Funktionsmessungen 56 6.3.1 Kardiale autonome Funktion: Herzratenvariabilität (HRV) 56 6.3.2 Sudomotorische autonome Funktion: Sympathetic Skin Response (SSR) 61 6.3.3 Vasomotorische autonome Funktion: Photoplethysmographie (PPG) 61 6.4 Symptomschwere und funktionelle Beeinträchtigung 62 6.4.1 Autonomes Outcome: Survey of Autonomic Symptoms (SAS) 62 6.4.2 Funktionelle Beeinträchtigung: modified Rankin Scale (mRS) 63 7. DISKUSSION 63 7.1. Zentrale Erkenntnisse 63 7.2 Autonome Funktionen 64 7.2.1 Kardiale autonome Funktion: Herzratenvariabilität (HRV) 64 7.2.2 Sudomotorische Funktionsmessung: Sympathetic Skin Response (SSR) 71 7.2.3 Vasomotorische Flussmessung 72 7.3 Symptomschwere und funktionelle Beeinträchtigung 73 7.3.1 Symptome des autonomen Nervensystems: Survey of Autonomic Symptoms (SAS) 73 7.3.2 Funktionelle Beeinträchtigung: modified Rankin Scale (mRS) 75 7.4 Limitationen und Ausblick 76 8. ZUSAMMENFASSUNG 78 8.1 Zusammenfassung 78 8.2. Summary 80 9. LITERATURVERZEICHNIS 82 10. ANHANG 109 10.1 Anhang I Fragebögen Klinischer Outcomes 109 10.2 Anhang II Demographische Daten 112 10.3. Anhang III Autonome Funktionsmessungen 116 10.4 Anhang IV Symptomschwere und funktionelle Beeinträchtigung 119 10.5 Erklärung zur Eröffnung des Promotionsverfahrens 122 10.6 Erklärung zur Einhaltung gesetzlicher Vorgaben 123 / Background: The occurrence of cardiac autonomic dysfunction following acute ischaemic stroke (AIS) worsens clinical outcome and is associated with an increased mortality. Therefore, we tested the hypothesis that heart rate variability (HRV) biofeedback can improve autonomic cardiac function post stroke. Methods/Design: We allocated (1:1) 48 AIS patients in a randomized fashion to undergo nine sessions of either HRV- or sham-biofeedback over three days in addition to standard stroke unit care. Autonomic function measurements, consisting of measurements of HRV, vasomotor (neurovascular control of arterial blood flow) and sudomotor (neural sweat gland control) function, were performed in all study participants before the start of the first biofeedback session and after completion of the last session. HRV was assessed using standard deviation of NN intervals (SDNN), a marker for primarily parasympathetically mediated cardiac modulation, Standard deviation of differences between adjacent NN intervals (SD of ΔNN) and root mean square of successive differences between normal heartbeats (RMSSD), a predominantly parasympathetic measure of HRV as well as via coefficient of variation of R-R intervals (CVNN), a composite parameter of sympathetic and parasympathetic activity. Moreover, frequency analysis of HRV components was carried out to further explore the mechanism whereby HRV biofeedback alters cardiac autonomic function. Both sympathetically regulated vasomotor and sudomotor functions were measured after sympathetic activation with vasomotor function recorded by photoplethysmography (PPG) of vasoconstrictory response (VCR) and sudomotor skin conductance changes of the sympathetic skin response (SSR) by conduction electrodes. Assessment of severity of autonomic symptoms via Survey of Autonomic Symptoms (SAS; TIS: Total symptom score) and functional deficits via modified Rankin scale (mRS) was performed before the start of the intervention and three months post intervention. The study protocol was registered at clinicaltrials.gov prior to commencement of study [clinicaltrials.gov identifier: NCT03865225]. Results: We included 48 AIS patients (19 females; ages median 69 [interquartile range 18.0] years. Implementation of HRV biofeedback into the setting of a stroke unit was feasible with no dropouts and high adherence and tolerability. Adding HRV biofeedback to stroke unit care led to an increased HRV under metronomic breathing (SDNN: 34.1 [45.0] ms baseline vs. 43.5 [79.0] ms post-intervention, p=0.015; SD of ΔNN: 29.3 [52.7] ms baseline vs. 46.4 [142.1] ms post-intervention, p=0.013; RMSSD: 29.1 [52.2] ms baseline vs. 46.0 [140.6] ms post-intervention, p=0.015; non-significant trend towards increase in CVNN: 4.1 [5.1] % baseline vs. 5.4 [7.2] % post-intervention, p=0.052) which was not seen after sham biofeedback (p=non-significant (ns)). In addition, frequency analysis of HRV revealed an increase in the low frequency band (LF) under metronomic breathing (484.8 [1941.4] ms2 baseline vs. 1471.3 [3329.9] ms2 post-intervention, p=0.019 and in total power (Total Power: 1273.9 [3299.2] ms2 baseline vs. 1771.5 [13038.8] ms2 post-intervention, p=0.022) after HRV biofeedback, which was not seen in the sham biofeedback group (p=ns). No changes in sympathetic sudomotor and vasomotor functions were detected in either study group (p=ns). HRV biofeedback led to a decrease of severity of autonomic symptoms (TIS: 7.5 [7.0] baseline vs. 3.5 [8.0] follow-up, p=0.029), which was absent in the sham biofeedback group. (p=ns). As expected both study groups showed an alleviation of functional deficits after three months (HRV biofeedback group, mRS: 2.0 [1.0] baseline vs. 0.0 [2.0] follow-up, p=0.023; Sham biofeedback group, mRS: 2.2 [2.0] baseline vs. 1.0 [2.0] follow-up, p=0.0005). Conclusions: Integrating HRV biofeedback into standard multidisciplinary stroke unit care for AIS led to improved cardiac autonomic function. This functional improvement was likely mediated by a predominantly parasympathetic mechanism and translated into sustained alleviation of autonomic symptoms.:1.EINLEITUNG 1 2. HINTERGRUND 4 2.1 Schlaganfall: Pathophysiologie und klinische Bedeutung 4 2.1.1 Definition und Klassifikation 4 2.1.2 Epidemiologie 7 2.1.3 Lokalisationsbezogene klinische Präsentation 9 2.1.4 Therapie 13 2.1.5 Risikofaktoren 16 2.2 Autonomes Nervensystem (ANS): Grundlagen und Beeinträchtigungen bei Schlaganfallpatienten16 2.2.1 Anatomische und physiologische Grundlagen 17 2.2.1.1 Sympathisches Nervensystem (SNS) 20 2.2.1.2 Parasympathisches Nervensystem (PaNS) 22 2.2.1.3 Enterisches Nervensystem (ENS) 23 2.2.2 Autonome Dysfunktion beim Schlaganfall 24 2.3 Herzratenvariabilität (HRV): Ein diagnostisches Target der kardialen autonomen Funktion 25 2.3.1 Definition 25 2.3.2 Relevanz 27 2.3.3 Anwendungsbereiche 28 2.4 Biofeedback: Allgemeine Therapieprinzipien und HRV-spezifische Anwendung 29 2.4.1 Definition 29 2.4.2 Anwendungsbereiche 29 2.4.3 Herzratenvariabilitäts-gestütztes Biofeedback 31 3. FORSCHUNGSLÜCKE („RESEARCH GAP“) 32 4. ZIELSETZUNG UND HYPOTHESEN 32 5. METHODIK 33 5.1 Ethik 33 5.2 Studiendesign und Messprotokoll 34 5.3 Patienten 36 5.3.1 Patientenrekrutierung 36 5.3.2 Einschlusskriterien 36 5.3.3 Ausschlusskriterien 36 5.3.4 Patienteninformation und -einverständniserklärung 37 5.3.5 Randomisierung 37 5.4 Funktionsmessungen 37 5.4.1 Funktionen des autonomen Nervensystems 37 5.4.1.1 Kardiale autonome Funktion: Herzratenvariabilität (HRV) 40 5.4.1.2 Sudomotorische autonome Funktion: Sympathetic Skin Response (SSR) 44 5.4.1.3 Vasomotorische autonome Funktion: Photoplethysmographie (PPG) 46 5.4.2 Symptomschwere und funktionelle Beeinträchtigung 48 5.4.2.1 Autonomes Outcome: Survey of Autonomic Symptoms (SAS) 48 5.4.2.2 Funktionelles Outcome: modified Rankin Scale (mRS) 49 5.4.2.3 Neurologisches Outcome: National Institutes of Health Stroke Scale (NIHSS) 49 5.5 Studienintervention: Herzratenvariabilitätsgestütztes Biofeedback 50 5.6 Statistische Analyse 51 6. ERGEBNISSE 52 6.1 Demographische Daten und Baseline-Charakteristika 52 6.2 Rekrutierung und fehlende Daten 54 6.3 Autonome Funktionsmessungen 56 6.3.1 Kardiale autonome Funktion: Herzratenvariabilität (HRV) 56 6.3.2 Sudomotorische autonome Funktion: Sympathetic Skin Response (SSR) 61 6.3.3 Vasomotorische autonome Funktion: Photoplethysmographie (PPG) 61 6.4 Symptomschwere und funktionelle Beeinträchtigung 62 6.4.1 Autonomes Outcome: Survey of Autonomic Symptoms (SAS) 62 6.4.2 Funktionelle Beeinträchtigung: modified Rankin Scale (mRS) 63 7. DISKUSSION 63 7.1. Zentrale Erkenntnisse 63 7.2 Autonome Funktionen 64 7.2.1 Kardiale autonome Funktion: Herzratenvariabilität (HRV) 64 7.2.2 Sudomotorische Funktionsmessung: Sympathetic Skin Response (SSR) 71 7.2.3 Vasomotorische Flussmessung 72 7.3 Symptomschwere und funktionelle Beeinträchtigung 73 7.3.1 Symptome des autonomen Nervensystems: Survey of Autonomic Symptoms (SAS) 73 7.3.2 Funktionelle Beeinträchtigung: modified Rankin Scale (mRS) 75 7.4 Limitationen und Ausblick 76 8. ZUSAMMENFASSUNG 78 8.1 Zusammenfassung 78 8.2. Summary 80 9. LITERATURVERZEICHNIS 82 10. ANHANG 109 10.1 Anhang I Fragebögen Klinischer Outcomes 109 10.2 Anhang II Demographische Daten 112 10.3. Anhang III Autonome Funktionsmessungen 116 10.4 Anhang IV Symptomschwere und funktionelle Beeinträchtigung 119 10.5 Erklärung zur Eröffnung des Promotionsverfahrens 122 10.6 Erklärung zur Einhaltung gesetzlicher Vorgaben 123

Heart rate variability (HRV) and music have been demonstrated to have a relationship in previous literature. The primary objective of this study is to further investigate that relationship by observing HRV during periods of listening to relaxing and exciting music and comparing the results to a baseline as well as the other condition. The secondary objective of this study is to investigate the efficacy and potential usage of the Polar H10 chest strap monitor in measuring HRV parameters. The results of the Polar H10 will be compared to the iWorx TA-220 and iWorx-ECG12, the existing gold standard in HRV and ECG recording. The data will be exported to Matlab and Excel and analyzed to see if particular types of music display any trends for these HRV parameters, as well as heart rate (HR). Polar data will be gathered and analyzed using the EliteHRV app. Analysis included Fast Fourier Transform (FFT), Low Frequency/High Frequency Ratio (LF/HF), standard deviation of NN intervals (SDNN). Data was gathered in 10 minute intervals of No Music, Relaxing Music, Exciting Music. Results showed notable changes in LF/HF ratio in both directions. SDNN and Mean RR interval had moderate decreases in both relaxing and exciting music, with Total Power having a significant decrease in both. Comparison of Polar H10 and iWorx-ECG data showed strong agreement in heart rate and RR interval data, but significant differences in other data. This suggests differences in calculation by the software used.

Heart rate variability (HRV) analysis has proved to be an important tool for assessing autonomic nervous system. For instance, it has been used during dipyridamole echocardiographic test to differentiate ischemic from nonischemic responses [6]. RR Interval analysis can provide additional information that can lead to early detection of a possible change in the activity of the autonomic nervous system. HRV analysis can be done using Wavelet Transform. This thesis presents a modification of an existing algorithm for extracting the R-R interval from EKG data sets and the use of wavelet transform (WT) technique to compute the timerequency domain energy quantities. The project used data obtained previously from a study of the effects of two pharmacological agents, atropine and propranolol, on laboratory rats. Results showed that the ratio of high frequency energy over the total energy (HF/total) of atropine treated rats was higher than baseline (control).

This report is the result of a thesis conducted at the consulting firm Bjerking AB andis the final part of the Bachelor Programme in Construction Engineering at Universityof Uppsala. The work aims to investigate the costs and how the choice of ventilationsystem affects building projects during a long-term period. This report covers costssuch as investment, maintenance and energy but also how they affect residents andbuilders. Building regulations for energy consumption are expected to be tougher;therefore a comparison of two different heat recovery ventilation systems(HRV-system) was made. One system is based on a centrally placed unit that coversthe whole buildings ventilation through vertical shafts. The second system is based onapartment placed unit that only covers the individual apartment’s ventilation. The unitmakes it possible for the individual user to control the ventilation flow.The method used for comparison of the costs was Life Cycle Cost (LCC). It results inthe total cost during a selected calculation period of 20 years, where yearly basedcosts as energy and maintenance is included. Two housing projects in central Uppsalawere chosen as a reference. They were similar in design but with the two differentsystems of ventilation. A questionnaire was handed out to provide experience fromresidents with apartment units. The results were used in the analysis of the systemsand to determine its pros and cons.Information of costs was collected from different companies and resulted intocustomized spreadsheets to determine the cost per apartment. The result shows thatthe difference in investment is not significant between the systems but is big inmaintenance and energy. The biggest difference is maintenance where the apartmentsystems many service points is increasing the cost. Energy consumption for theapartment system leads to higher energy costs than with a central system, even if thecontrol function is used. The explanation is that a central systems fans have lowerpower usage and the heat recovery is more efficient then an apartment system.The conclusion is that a central system has a lower total cost compared to anapartment system during the calculation period. A reason for choosing the apartmentsystem would be if a need to maximize living space is a priority, and the developingphase of the project is well thought thru.Keywords: LCC-analysis, HRV-systems, Central unit, Apartment unit.

abstract: Transcranial focused ultrasound (tFUS) is a unique neurostimulation modality with potential to develop into a highly sophisticated and effective tool. Unlike any other noninvasive neurostimulation technique, tFUS has a high spatial resolution (on the order of millimeters) and can penetrate across the skull, deep into the brain. Sub-thermal tFUS has been shown to induce changes in EEG and fMRI, as well as perception and mood. This study investigates the possibility of using tFUS to modulate brain networks involved in attention and cognitive control.Three different brain areas linked to saliency, cognitive control, and emotion within the cingulo-opercular network were stimulated with tFUS while subjects performed behavioral paradigms. The first study targeted the dorsal anterior cingulate cortex (dACC), which is associated with performance on cognitive attention tasks, conflict, error, and, emotion. Subjects performed a variant of the Erikson Flanker task in which emotional faces (fear, neutral or scrambled) were displayed in the background as distractors. tFUS significantly reduced the reaction time (RT) delay induced by faces; there were significant differences between tFUS and Sham groups in event related potentials (ERP), event related spectral perturbation (ERSP), conflict and error processing, and heart rate variability (HRV). The second study used the same behavioral paradigm, but targeted tFUS to the right anterior insula/frontal operculum (aIns/fO). The aIns/fO is implicated in saliency, cognitive control, interoceptive awareness, autonomic function, and emotion. tFUS was found to significantly alter ERP, ERSP, conflict and error processing, and HRV responses. The third study targeted tFUS to the right inferior frontal gyrus (rIFG), employing the Stop Signal task to study inhibition. tFUS affected ERPs and improved stopping speed. Using network modeling, causal evidence is presented for rIFG influence on subcortical nodes in stopping. This work provides preliminarily evidence that tFUS can be used to modulate broader network function through a single node, affecting neurophysiological processing, physiologic responses, and behavioral performance. Additionally it can be used as a tool to elucidate network function. These studies suggest tFUS has the potential to affect cognitive function as a clinical tool, and perhaps even enhance wellbeing and expand conscious awareness. / Dissertation/Thesis / Doctoral Dissertation Bioengineering 2020

This paper explains the concept of emotion, emotion dimension and how emotions relate to the central nervous system. Additionally, there is research that were the inspiration for this work, the basic types of emotions and their physiological responses. The following are ways to monitor signals caused by emotions. The practical part deals with the sensing signals from the periphery of the body, specifically the EDA, EMG, EOG and PPG. As stimuli to elicit these signals were used photography, music and film. The signals were processed using the program developed in Matlab. Program specific values obtained were statistically analysed.

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Nature and greenspaces have been enjoyed throughout history and used for relaxation purposes. Several theories, such as biophilia and stress recovery theory, suggest nature’s ability to improve stress recovery. Even though stress helps detect danger and enhances alertness, it causes fatigue and distortive cognitive functions if prolonged. Nature-based intervention such as Shinrin-yoku or forest bathing, which refers to relaxing walks in forest environments, has been recently researched and used to reduce stress in individuals. The current study is an experimental study aimed at whether attendance in nature is beneficial for stress recovery. Ten subjects were divided into an experimental group (walks in nature) and a control group (walks in a city environment). They were tested for stress levels using heart rate variability (HRV) and the Karolinska exhaustion disorder scale (KEDS) before and after the walks. The result demonstrated no significant differences in stress recovery for both measurements before and after walks in nature compared to walks in a city environment. Despite that, it did not reject nature’s positive impact on stress recovery. Therefore more research on nature-based intervention and stress recovery is required.